{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "# Predict Blood Donations\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The objective of this notebook is to predict if a blood donor will donate within a given time window, given parameters such as months  since last donation (recency), number of donations made (frequency) , total volume of blood donated (cc) [monetary], months since first donation (time).\n",
    "\n",
    "This is a warm-up problem in an ongoing competition at https://www.drivendata.org/competitions/2/warm-up-predict-blood-donations/page/7/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix,log_loss\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "from imblearn import over_sampling as os\n",
    "from imblearn import under_sampling as us\n",
    "from imblearn import pipeline as pl\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "from collections import Counter\n",
    "from imblearn.combine import SMOTETomek\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.utils import to_categorical\n",
    "\n",
    "RANDOM_STATE = 2017\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from jupyterthemes import jtplot\n",
    "\n",
    "jtplot.style('grade3', context='poster', fscale=1.5)\n",
    "jtplot.style(ticks=True, grid=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"transfusion.data.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recency (months)</th>\n",
       "      <th>Frequency (times)</th>\n",
       "      <th>Monetary (c.c. blood)</th>\n",
       "      <th>Time (months)</th>\n",
       "      <th>whether he/she donated blood in March 2007</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>12500</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>3250</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>4000</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>5000</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>6000</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Recency (months)  Frequency (times)  Monetary (c.c. blood)  Time (months)  \\\n",
       "0                 2                 50                  12500             98   \n",
       "1                 0                 13                   3250             28   \n",
       "2                 1                 16                   4000             35   \n",
       "3                 2                 20                   5000             45   \n",
       "4                 1                 24                   6000             77   \n",
       "\n",
       "   whether he/she donated blood in March 2007  \n",
       "0                                           1  \n",
       "1                                           1  \n",
       "2                                           1  \n",
       "3                                           1  \n",
       "4                                           0  "
      ]
     },
     "execution_count": 406,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.columns = ['months_since_last_donation', 'num_donations', 'vol_donations', 'months_since_first_donation', 'class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>months_since_last_donation</th>\n",
       "      <th>num_donations</th>\n",
       "      <th>vol_donations</th>\n",
       "      <th>months_since_first_donation</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>12500</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>3250</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>4000</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>5000</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>6000</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   months_since_last_donation  num_donations  vol_donations  \\\n",
       "0                           2             50          12500   \n",
       "1                           0             13           3250   \n",
       "2                           1             16           4000   \n",
       "3                           2             20           5000   \n",
       "4                           1             24           6000   \n",
       "\n",
       "   months_since_first_donation  class  \n",
       "0                           98      1  \n",
       "1                           28      1  \n",
       "2                           35      1  \n",
       "3                           45      1  \n",
       "4                           77      0  "
      ]
     },
     "execution_count": 416,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<br>\n",
    "<br>\n",
    "class 1 => the donor donated blood in March 2007 [let's call them donors]\n",
    "<br>\n",
    "class 0 => the donor did not donate blood in March 2007 [ let's call them non-donors ]\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "** Number of instances of each class **\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    570\n",
       "1    178\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 417,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['class'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<br>\n",
    "<br>\n",
    "we see that there is a class imbalance problem here. We can fix this issue by oversampling the minority class (1) or undersampling the majority class.\n",
    "<br>\n",
    "<br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "---\n",
    "## EDA\n",
    "---\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "Let's explore the dataset and find out if we can get some interesting insights.\n",
    "\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def ecdf(data):\n",
    "    \"\"\"\n",
    "    returns empirical CDF of a data\n",
    "    \"\"\"\n",
    "    n= len(data)\n",
    "    x = np.array(sorted(data))\n",
    "    y = np.arange(1,n+1)/(n*1.0)\n",
    "    return x,y\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AZEWAAQAJIhQKqKG5WvVNVWporlJjc40aA7VqbK5VY6BGTc21qmvartrGbaqp\nL1Nl3RaF7ZByMorUvWCohvQ8ReeMvkVd8gYk5Koi0Wzb1gsztuiRt9br0rFd9bPz+ijVn3hTXwAA\nANB+BBgAEEds21ZFzQZtrjDaun25ttVs0PbaTappKFNzsH7Hfim+dKWn5ig9JVtpqdnOZUqWstI6\nqWv+QOVkFKlTdg8V5PRKyJVE9sa2bT04da3emFumSVcP1vgRBW6XBAAAgBggwAAAl4XDQa3aOkfL\nNn2qVVtmqaahXHlZxereaYi6dhqooT3HKTezizLT8pWZlqeM1Dyl+NPcLtsVobCte19ZrY8XVejR\nnw7TsF7ZbpcEAACAGCHAAACX1DZW6KuVr2v+6jfUFKjXoO5jdfpR16tf19HKSu/kdnlxJxAM6+4X\nVmreqmo9fv1wDSjOdLskAAAAxBABBgDEWENTtWYtfU5frJiszjm9dcrwH2p479OVmsIJ+Z40BkK6\n/enlWrW1Xo/fMFy9i2jWCQAA0NEQYABAjNi2rflr3tL0bx5VTkaRLj7hbg3qfmLCN9Q83OqbQrr5\nH0tVWtmsv91whLp16pjTZwAAADo6AgwAiIGK2k16c+69KqlcodOO/KlGDbhAHg+rZuxLdX1QN/3d\nqLE5rMdvGK7C3FS3SwIAAIBLCDAA4DCybVsL176jd+Y9oL5dj9VPzn5OORmFbpeVEKrqA7r+L0uU\n6vfqL9cPU15mitslAQAAwEUEGABwmDQH6vXvr+7X0o2faMLRv9DR/c9nukg7NQXC+q8nlyktxas/\nX2spO53/rgAAADo63hECwGGwrWa9Xv30TknSjyY8qcLcvu4WlEDCYVt3v7BSFbUBPfmzIwgvAAAA\nIIkAAwAOuWWbZmranN9rQLfjdd7oW1ldZD/9+c11mreqSk/+bITys5k2AgAAAAcBBgAcIuFwSB8v\n+rtmL3tBpx95vcYM/jZTRvbTSzO3aPLnJfrr9cPUszDd7XIAAAAQRwgwAOAQqGvcrimzf6uyqjX6\n7vj/U++io9wuKeF8tGCbHpq2Tn+8ZrCG985xuxwAAADEGQIMADhIm7cZvfr5ncrN7KofTniSVUYO\nwII1Nfr1cyt080V9dcoRBW6XAwAAgDhEgAEAB2Heqml6d96DGjXgQn1r5I3yefmzur/WlTXo5n8s\n1eUnF+uyE7u5XQ4AAADiFO+0AeAABIJNemfeA1qyfrrOH3O7jugzwe2SEtL22oBuesLouMF5uuGc\n3m6XAwBvXxNfAAAgAElEQVQAgDhGgAEA+6m2YZte/vQ2NTTX6Pvfelxd8ge4XVJCCoZs3fHschXm\npurXVwyU10vDUwAAAOwZAQYA7IeSypV6aeYtKsjppSvHPaD0VJpNHqi/vr1e60ob9Mwvj1Sq3+t2\nOQAAAIhzBBgA0E7rSr/WSzNv0fDe39JZx9xMv4uD8PHCCj0/Y4se+8kwFeamul0OAAAAEgDvvgGg\nHdaUfKWXZ96q44deoVOG/0AeD9MdDtT6sgb99sWV+tl5fTSyf67b5QAAACBBEGAAwD6s3vqFXv70\nVp007BqdNOwat8tJaA1NId361DKNHZqv75zMiiMAAABoPyYdA8BelFSu1Kuf3UF4cQjYtq17X12t\nsC3defkARrEAAABgvxBgAMAe1DSU7+h5caL1PbfLSXivflaiGYsr9Mdrhigzzed2OQAAAEgwBBgA\n0IZAsEkvz7xNnXP66Kxjbma0wEFauK5GD05bq7smDlTfrhlulwMAAIAERIABAG2Y/s2jagzU6tKx\n97DayEGqqAno9qeX6/KTuun0ozq7XQ4AAAASFAEGALSybOMMzVs9TZeccLfSU7PdLiehhcO2fvP8\nCnXvnKYbz+3tdjkAAABIYHysCABRqupL9MYX9+q0EdepuGCo2+UkvBdmbJHZWKfnbj5Sfh+ZOQAA\nAA4c7yYBIMK2bb059151L7B03JCJbpeT8MyGWj367/X61eUD1DU/ze1yAAAAkOAIMAAgYv6at7S5\nwui80bfJ4+HP48GobwrpV/9aoQvGdNH4EQVulwMAAIAkwDt0AJBUXV+mD+Y/otOPul65mV3cLifh\n/en1NfL7PLrpgj5ulwIAAIAkQYABoMOzbVtvf3W/ijsN0dH9L3C7nIT3/tflevfrcv3P1YOUnupz\nuxwAAAAkCQIMAB3ekg3TtabkS507+hZ5PB63y0lomysaNenV1fr5+X00sDjL7XIAAACQRAgwAHRo\n9U2VenfeQzp1xLXqlN3D7XISWjBk665/rdCo/rn69ond3C4HAAAASYYAA0CH9t7XDys/q1ijB13m\ndikJ78n3N2jL9ibdNXEAI1kAAABwyBFgAOiwVm6ZpSUbpuu80bfL66VXw8GYt6paT03frLuvGKT8\n7BS3ywEAAEASIsAA0CE1Ber17y//VydaV6tLfn+3y0loVfUB/fq5FbpqXLHGDM5zuxwAAAAkKQIM\nAB3SRwseV6o/UydaV7tdSkKzbVuTXl6twtwU/eTsXm6XAwAAgCRGgAGgw9lQtkBfrZqi80bfJr8v\n1e1yEtqU2aWas7xSv//uYPl9/JcCAACAw4d3mwA6lOZgg9784o8aPfAS9Sw8wu1yEtrqrfV6YOpa\n3XJJf/UsTHe7HAAAACQ5AgwAHcq78x6Ux+PR+COvdbuUhNYUCOtX/1qhU0cU6Jxji9wuBwAAAB0A\nAQaADmPBmre1eP10XXLC75Tqz3C7nIT2yFvr1NAc0i2X9nO7FAAAAHQQfrcLAIBYKKtarbe/+pPO\nGvVLVh05SJ8u2a7Jn5fobzcMV3Y6/40AAAAgNhiBASDpVdeX6oUZ/6VhvU/TUf3OdbuchFZe3azf\nvbhS157ZS0f0yXG7HAAAAHQgBBgAklpDU7We/+Q/VZTXX+cce4s8Ho/bJSWscNjW3c+v1IDiTF19\nane3ywEAAEAHQ4ABIGk1NFXrxZn/rbSULF069h75vEx3OBjPfbJZyzbV6bdXDpTPSxAEAACA2CLA\nAJCUttdu1lPTfyJJmnjyfTTtPEhLNtTqL29v0K8mDlCXvDS3ywEAAEAHRIABIOmsL/tGT31wnQpz\n++q74/9PmWl5bpeU0OoaQ7rrXyt00XFdNO6IArfLAQAAQAfFeGoASSMYataMRU9q9rIXdfyQ72j8\niGvl9frcLivh3f/6GqX6Pfr5BX3cLgUAAAAdGAEGgKRQWrlaU+f8Tg3NNbpq/EPq0+Vot0tKCu/O\nK9cH88v11E1HKj2FMAgAAADuIcAAkNBsO6y5y1/Rhwse17Bep+rMUb9Uemq222UlhU3bGvWHyav1\niwv6akBxptvlAAAAoIMjwACQsBqaqjV1zj3auG2RLjz+VxrW6zS3S0oawVBYdz23QscMyNWlY7u6\nXQ4AAABAgAEgMW2pWKpXP/uVstI76ccT/qm8rG5ul5RUnnhvo0q2N+mBHwyVx8OSqQAAAHAfAQaA\nhLOhbIFemHGzRvQ9SxNG/lw+X4rbJSWVr1ZW6ZkPN+vP11rKz+a5BQAAQHwgwACQUFrCi9GDLtP4\nEdcyOuAQq6wL6DfPr9R3xxdr9CCWnwUAAED88LpdAAC0V8n2FYQXh5Ft25r0ymoV5aXqurN6uV0O\nAAAAsAsCDAAJoSlQp8mf36Vhvb9FeHGYvPpZib5YXqV7rhokv4//HgAAABBfeIcKIO7Ztq23vrhP\nfl+azjz6JsKLw8BsqNVD09bqjsv7q2dhutvlAAAAALshwAAQ975ePU0rt3yuS8feoxR/mtvlJJ3a\nhqDueHa5Ljiui84YWeh2OQAAAECbCDAAxLXaxgpN/+YxTTj6JnXO7e12OUnHtm39/uVVykrz6aYL\n+rpdDgAAALBHrEICIK59tOCvKsrtp6P6ne12KUlp8uclmrOsSk//coTSUsi0AQAAEL94twogbm3a\ntlgL1r6rM0fdJI+HP1eH2tKNtXpw6lrd8e3+6l2U4XY5AAAAwF5xRgAgLtl2WO/Oe0gj+52j4oKh\nbpeTdGobgrrjmeU6f0wXnXE0fS8AAAAQ/wgwAMQls/FjlVev0/gR17pdStKxbVv/88pqZaT59MsL\n+7pdDgAAANAuBBgA4o5thzVz0T81ZvDlykrv5HY5SWfyrBLNWrpd935vMH0vAAAAkDB45wog7piN\nH6uqvkTHDb7c7VKSzrKNdXpo6lrd8e0B9L0AAABAQiHAABBXdo6++LYy0nLdLiep1DYGdfszy3Tu\n6C6aQN8LAAAAJBgCDABxZefoi4lul5JUbNvWvTv6XvRxuxwAAABgvxFgAIgbtm3rsyXPaPSgSxl9\ncYi9NqtEn5ntmvS9wUpP8bldDgAAALDfCDAAxI3VW+dqW816jaH3xSG1bGOdHpy6VrdfNkB96HsB\nAACABEWAASBufL70OR3V9xxWHjmEahuDuuPZ5Tr32CKdOYq+FwAAAEhcBBgA4sLmbUbry+br+KFX\nuF1K0mjpe5GW4tUvL+rrdjkAAADAQSHAABAXZi19TlbPU9Upu4fbpSSN12eX6jOzXffS9wIAAABJ\ngAADgOsqajZo6aYZOmHolW6XkjSWb6rTA1PW6LbL+qtPF/peAAAAIPERYABw3aylL6hvl1EqLhji\ndilJoa4xpNufWa5zji3SWaOK3C4HAAAAOCQIMAC4qrZhmxasfUcnDL3K7VKSgm3b+sOrTt+L/6Tv\nBQAAAJKI3+0CAHRsc1e8oqK8vurX9Vi3S0kKb31Rpk8WV+jpm0bQ9wIAAABJhREYAFzTFKjTVyun\naOzQq+TxeNwuJ+GtK23Qfa+v0X9d1E/9uma6XQ4AAABwSBFgAHDNVyunKDMtT0N7jne7lITXFAjr\njmeX65RhnXT+GPpeAAAAIPkQYABwRVOgXrOXvaCx1tXyepnqcLAefnOd6ptCuu2y/oxmAQAAQFKi\nBwYAV3yx4hWlpWTrqL5nuV1KwvtkUYVem1WiJ24cruwM/qwDAAAgOTECA0DMNTRXa9bSF3TK8B/I\n6+WE+2CUbG/SPS+t0k/P7qXhvXPcLgcAAAA4bAgwAMTcnGUvKSejUMN7n+52KQktGLJ11/MrNKxX\nlq4a193tcgAAAIDDigADQEzVNJRr7vKXNe6IH9L74iD944ONWl/WqN9cMVBeL30vAAAAkNwIMADE\n1AfzH1G3TkNYeeQgzVtVpX9+sEm/vWKgOuekul0OAAAAcNgRYACImTUlX8ps+EhnH/OfrJRxEGob\ng7r7hZW6clyxjhuS73Y5AAAAQEwQYACIiVAooHe+ekBjBl+uorz+bpeT0B6auk7Z6X5dd1Yvt0sB\nAAAAYob2/wBi4lPzjJqDDTpl+PfdLiWhfbpku/79VZme+sUIpfrJoAEAANBx8O4XwGG3puQrfbbk\nGZ0/5g6lpmS6XU7CqqoPaNIrq/TDM3pqcI8st8sBAAAAYooAA8BhVdtYoamzf6ex1tXq32202+Uk\ntPtfW6uivFRdcxpLpgIAAKDjIcAAcNiEwkFNmf1bFeT0YurIQfpwwTZ9tHCbfvOdgfL7+NMNAACA\njod3wQAOC9sO6425k1RRs1EXn3C3vF6f2yUlrIqagP7w6mr95Kze6t+NKTgAAADomGLaxNOyLL+k\nP0m6Wk54MlnSDcaYxjb2LZb0iKRxkjySZkq60RizMXYVAzgQtm3rva//rNVb5+qa0x9TTkah2yUl\nLNu29YfJq9WnKENXjCt2uxwAAADANbEegXGHpFMljZA0SNIwSfftYd/HJKVK6iepl6Q6Sf+IQY0A\nDoJt2/pwwV/0zZq3dMUp96tzTm+3S0po784r1+xllfr1FQPk83rcLgcAAABwTawDjB9JmmSM2WSM\nKZN0t6T/sCyrrbHlAyS9YoypMcbUS3pe0pGxKxXA/gqHQ/r3l/fp61XTdMW4B1RcMNTtkhJaaVWT\n/vf1NfrZuX3UqzDD7XIAAAAAV8VsCollWflyRlLMj9o8T1KOpL6SVrW6ywOSLrMsa5qkkJxpJ28c\n/koBHIhAsEnT5tyj9eULdPVpj6hr/kC3S0potm3r3ldWa0iPLF06tqvb5QAAAACui2UPjJzIZWXU\ntspWt0X7VNIPJFVIsiUtkDRhbw9gWda1kq6N3ubz+XzDhw8/kHoBtFNNQ7le+fR2NQZqdc3pf1FB\ndg+3S0p4Hy+s0JcrqvTiLSPlZeoIAAAAENMAoyZymSdpa+R6fqvbJEmWZXklfSDpNUnnyBmBcYuk\njy3LGmmMCbT1AMaYv0n6W/S2iRMn5mnX0ATAIbR1+wq9NPNWdc7tpSvG/UkZqblul5Tw6ptCemDq\nWn3/jJ7q0Tnd7XIAAACAuBCzHhjGmEpJGySNjNo8Sk54sbbV7gWS+kj6szGm1hjTIGdKyTA5vTEA\nxIFlm2bq6Q+v16DuY3XFKYQXh8qT729UWopX3x3f3e1SAAAAgLgR02VUJf1d0u2WZc2UFJDTxPMp\nY0woeidjTLllWSslXW9Z1m/kjMD4haTt2j3sABBjtm1r1tLn9PHCJ/StkTdq9KDL5PEwzeFQWL21\nXs9/skUP/WioUv2x7rMMAAAAxK9YBxiTJBVKWixn9Merkm6VJMuy/ipJxpifRPa9UM6oi42RfRdJ\nOs8Y0xjjmgFEaQrU6Y25k7S25CtdfvIfNLD4BLdLShq2bet/X1ujU0cU6Lgh+fu+AwAAANCBxDTA\nMMYEJf088tX6tp+0+n6JpLNiVBqAdiirWq1XPrtTKb50/XDCk+pEs85D6t155TIba/XyrSP3vTMA\nAADQwcR6BAaABLVo3ft664s/yup1ms4+5mal+NPcLimp1DYE9dAb6/TjM3upSx7PLQAAANAaAQaA\nvQoEG/XBN49q/uq3dOaom3R0//Ppd3EYPP7uBnXK8mviSd3cLgUAAACISwQYAPZoY/kiTZvze8nj\n0TWnP6buBUPdLikpLdtYp1c/26q//HS4/D4adwIAAABtIcAAsJuG5mp9svDvmrdqqo4deIlOPfI6\npfjT3S4rKYXDtv742mqdNapII/uzDC0AAACwJwQYAHYIhYOav/pNfbLo78rJKNTVpz6sXkVHul1W\nUps2t1TrSht0//cZ3QIAAADsDQEGAIXCQS1a955mLv6nAsFGnTL8+xo14CJ5vT63S0tqtQ1BPfrW\nev30nN4qyElxuxwAAAAgrhFgAB1YY3Otvl49TXOXv6pgqFEnDL1Sxw66VKn+DLdL6xCe+WizCnJS\ndPHxXd0uBQAAAIh7BBhAB7SlYpm+WjVFi9e9r+yMzjrRukpH9juH4CKGyqqa9cKMLfr9dwfJ52VV\nFwAAAGBfCDCADiIQbNTi9dM1b9UUbdm+TIOKx+rSE+/RgG7HyeNh5YtY+/v7GzWkR5ZOGd7J7VIA\nAACAhECAASS56voyfbnyNX29aqp83hSN7H+eLh17j/KyurldWoe1rqxB0+aU6i8/HSaPh9EXAAAA\nQHsQYABJanPFUs1Z9pLMhg/VNX+Qzhx1k6yep8rno1mk2/769gadMDSfZVMBAACA/UCAASSZzduM\nPlr4uNaWztOQHqfo6lMfVs/CEXzSHyeWrK/VRwu26V83szwtAAAAsD8IMIAkUdNQrve+/j8t3fiJ\njugzQdef84I6ZfdwuyxEsW1bj7y1TmcdU6SBxVlulwMAAAAkFAIMIMHZtq35q9/UB988qm6dBuvH\nE55Sl/z+bpeFNsxZXqVv1tTo1dsGul0KAAAAkHAIMIAEFgg2adrc32v11rn61lE3amT/85gqEqfC\nYVuPvrVel53YTcUFaW6XAwAAACQcAgwgQdU1btfLn96mhuZq/fCMJ1WQ09PtkrAXH3yzTRu3Nerh\nay23SwEAAAASktftAgDsv4amaj09/afyeVP0/dMfJ7yIc4FgWH95e72+O7678rNZBQYAAAA4EIzA\nABJMOBzU67PvVnpqjq4c9yf5fUxHiHdTZpeqoTmsK08pdrsUAAAAIGExAgNIMB8t/JtKK1fpshMn\nEV4kgPqmkJ58f6N+NKGnMtJ8bpcDAAAAJCwCDCCBrNw8S3OXv6JLT/y9cjOL3C4H7fD67BKlpXp1\n0XFd3C4FAAAASGgEGECCCIdDmv7NYzpuyHfUq3CE2+WgHZqDYT338WZ979Tu8vv4cwsAAAAcDN5R\nAwli0br3VNNYrrFDr3S7FLTTm1+Uybal80Yz+gIAAAA4WAQYQAIIhpr1yaInNXbod5WemuN2OWiH\nYMjWMx9u0lXjuisthT+1AAAAwMHiXTWQAL5eNU1hO6jRgy51uxS00wfzy1XbENQlY7u6XQoAAACQ\nFAgwgDhn22HNWva8TrSuVoo/3e1y0A7hsK2nPtykiScXK5OVRwAAAIBDggADiHMbyheqtrFCw/uc\n4XYpaKcZi7dr6/YmXX5yN7dLAQAAAJIGAQYQ55asn66B3Y5XRmqu26WgHWzb1lPTN+qSE7opLzPF\n7XIAAACApEGAAcSxcDgos+EjDet9utuloJ2+WFGllVvqdeW4YrdLAQAAAJIKAQYQx9aWfq3mYIMG\n9zjJ7VLQTv/8YJPOH9NFhbmpbpcCAAAAJBUCDCCOLV7/vgZ1P1Gp/gy3S0E7LFhTo/lrqnX1qd3d\nLgUAAABIOgQYQJwKhpq1dOMMDWf6SMJ46sNNOnNUkboXsFoMAAAAcKgRYABxal3pPNl2WAOKj3e7\nFLTD8s11+txs1zWnMfoCAAAAOBwIMIA4tb7sG/UqHCG/j14KieDp6Zs0fkSB+nXNdLsUAAAAICkR\nYABxakP5QvUsHOF2GWiH9WUNmv7NNl1zWg+3SwEAAACSFgEGEIdCoYA2VyxRr6Ij3S4F7fDijC06\ndmCerF7ZbpcCAAAAJC0CDCAObdm+TOFwSD0KhrldCvahuj6oN78s0xXjit0uBQAAAEhqBBhAHNpY\nvlDdOg1Wip/VLOLdtDml6pKXqhOG5LtdCgAAAJDUCDCAOLShfIF6FTJ9JN4FQ7Ze/myLJp5ULK/X\n43Y5AAAAQFIjwADijG3b2lC+kP4XCWDG4grVNoR07ugit0sBAAAAkh4BBhBnKmo3qL6pkhVIEsAL\nM7bowuO6KDPN53YpAAAAQNIjwADizIayBSrI7qns9AK3S8FemA21Wri2Rt8+qZvbpQAAAAAdAgEG\nEGeYPpIYXpy5ReOOKFD3AhqtAgAAALFAgAHEma3bl6l7geV2GdiL8upmvT9/myaezNKpAAAAQKwQ\nYABxJBwOqbx6vQpz+7ldCvbitc9LNKBbpo7un+N2KQAAAECHQYABxJHKus0KhZtVlEeAEa+aAmFN\nnrVVE0/uJo+HpVMBAACAWCHAAOJIWdUaZaUXKDMtz+1SsAfvf10ujzyacHSh26UAAAAAHQoBBhBH\nyqrXqjC3r9tlYA9s29YLM7fokrFdlernzycAAAAQS7wDB+JIWdUaFdH/Im59vbpaa0sadMkJXd0u\nBQAAAOhwCDCAOFJevYb+F3HshRlbdMbRhSrMTXW7FAAAAKDDIcAA4sTOFUj6ul0K2rBpW6NmLt6u\n75zcze1SAAAAgA6JAAOIE6xAEt9e/WyrjuyXo6E9s90uBQAAAOiQCDCAOMEKJPGrMRDSG1+U6dtj\nGX0BAAAAuIUAA4gTrEASv6Z/UyG/16PxIwrcLgUAAADosAgwgDjBCiTx67XPt+qC47oohaVTAQAA\nANfwbhyIE6xAEp+Wb67TovW1uuj4Lm6XAgAAAHRoBBhAHGAFkvj12uclGjs0X90L0t0uBQAAAOjQ\nCDCAOMAKJPGprjGkd+aV6RKadwIAAACuI8AA4kBF7Ualp+awAkmceXtemXIz/Ro7NN/tUgAAAIAO\njwADiAOVtVuUn1XsdhmIYtu2Xvu8RBcd31U+r8ftcgAAAIAOjwADiANV9VuVn9Xd7TIQZeHaWq0p\nadAFY2jeCQAAAMQDAgwgDlTWbmYERpyZPGurxo8oUGFuqtulAAAAABABBhAXKuuYQhJPKusCmv7N\nNl1yQle3SwEAAAAQQYABxIHKui3KzybAiBdvfVGm4k5pOnZgrtulAAAAAIggwABc1hSoU0NzNT0w\n4kQ4bOu1WSW6ZGw3eTw07wQAAADiBQEG4LLK2s2SpLzMbi5XAkn6cmWVSiubdM6xhW6XAgAAACAK\nAQbgssq6LcpO76wUf5rbpUDS5M9LdMbRhcrLTHG7FAAAAABRCDAAl9HAM36UVTVrxuIKmncCAAAA\ncYgAA3BZZd0W5RFgxIWpc0o0sDhLw3tnu10KAAAAgFYIMACXVdZtZgWSOBAK25o6p1QXn9CV5p0A\nAABAHCLAAFxWWbeVFUjiwJxllaqqD2rC0Z3dLgUAAABAGwgwABfZtk0PjDgxdU6pzhjZWdnpfrdL\nAQAAANAGAgzARfVNlQoEGwgwXLatplkzFm/XRcfTvBMAAACIVwQYgIsq67bI4/EpL7OL26V0aG9+\nUaY+XdJ1BM07AQAAgLhFgAG4qLJui3IziuT1Mm3BLbZta9qcUl10HM07AQAAgHhGgAG4iBVI3Ddv\nVbW2bm/SWccUul0KAAAAgL0gwABcVFlLA0+3TZldqlOPLFB+VorbpQAAAADYCwIMwEXV9SXKy+zm\ndhkdVlV9QB8t3EbzTgAAACABEGAALqppKFcODTxd885X5eqSl6pR/XPdLgUAAADAPhBgAC6qbihV\nbga9F9xg27amzC7Rhcd1lddL804AAAAg3hFgAC4JBBvV2FyjnAxGYLhh8fparS1t0Lmji9wuBQAA\nAEA7EGAALqlpKJMk5WQyAsMNU2aX6qRhnVSYm+p2KQAAAADagQADcEl1Q5lSfOlKT8lxu5QOp64x\npPfnl9O8EwAAAEggBBiAS2rqy5WTUSiPh/4Lsfb+/HLlZPh1/JB8t0sBAAAA0E4EGIBLahpKWYHE\nJVNml+iCMV3ko3knAAAAkDAIMACX1DSUKyeDBpKxtnxznczGOp0/hvAIAAAASCQEGIBLqutLlZtJ\ngBFrU2eX6rjBeSouSHO7FAAAAAD7gQADcElNQxkjMGKsMRDSO/PKaN4JAAAAJCACDMAlNQ1lyiXA\niKmPFlTI7/Po5GGd3C4FAAAAwH4iwABcEA4HVdtYwQiMGJsyu0TnHttFKX7+9AEAAACJhnfxgAtq\nGytk22Hl0AMjZtaVNejr1TW68HiadwIAAACJiAADcEFNQ5k8Hp+y0pjKECtTZ5fq6P456lOU4XYp\nAAAAAA4AAQbggur6MuVkdJbX63O7lA4hEAzrrS9Lad4JAAAAJDACDMAFrEASWzOXbFcwZOvUIwvc\nLgUAAADAASLAAFzACiSxNWV2ic4aVaT0FEa8AAAAAImKAANwgTOFhAAjFrZUNGnO8ipdRPNOAAAA\nIKERYAAuqGkoYwWSGHljbqmsnlka1D3L7VIAAAAAHAQCDMAF9MCIjVDY1rS5NO8EAAAAkgEBBhBj\ntm07PTAymdJwuM1eVqmahqDOGFnodikAAAAADhIBBhBjDc3VCoaalZPBSfXhNmV2ic4YWaisdJp3\nAgAAAImOAAOIsdqGcklSdjoBxuFUXt2sT5dsp3knAAAAkCQIMIAYq22sUHpKtlL8aW6XktTe+qJM\nfbtkaHjvbLdLAQAAAHAIEGAAMfb/7N15lJ13fR/+9yyaXbtsSTZewOtj2YAxxjZeMGtMCJiEFJOE\nNG1CiRsamrSnSUP7O4c2p/m1SWiTQBp+QFNCSEvCErMEA4ZgLG8QbAy2fMHgTV60zSbNnZk7muX+\n/pBMZFnLzOjOvXdmXq9zdEZzn2fu87aOH8N96/t8vuXx/vR2rW90jCVtZqaaz35zV958+ca0tLQ0\nOg4AAFADCgyos3JlMH3dCoyFdO8j+7J77/5cd4nHdAAAYKlQYECdlSsD6eta1+gYS9pNd+/KKy9a\nn9U9KxodBQAAqBEFBtRZeXwgfR4hWTDDo5P5+vcGc73hnQAAsKQoMKDOypUBj5AsoJvv6c+mtZ25\n5KxVjY4CAADUkAID6uzAIyQKjIVQrVZz09278ubLTza8EwAAlpj2el6sKIr2JO9L8os5UJ58Osm7\nSqVS5SjnvyHJ7yY5L8lIkveVSqU/qFNcWBCjhngumO8+OpIn+it5w0s9PgIAAEtNvVdgvCfJK5Nc\nlOScJBck+f0jnVgUxeuSfCjJv0uyOsm5SW6uT0xYGJNTlUxMjhriuUD+9u5dufbCdVm30vBOAABY\nauq6AiPJO5L8VqlUeipJiqJ4b5JPFkXxm6VSafqwc383ye+WSqWvHfx+X5IH6pYUFkC5MpAk6euy\nvVVx+kIAACAASURBVGet7R2bzNe+O5D//itFo6MAAAALoG4FRlEUa5KcluS+Q16+N8nKJGcmefiQ\nc3uTXJrk5qIovp9kbZJvJvnXpVLp0WNc451J3nnoa21tbW1btmyp0T8FnJjy+EDaWlekq2Nlo6Ms\nOV+6pz8nr+7IS882vBMAAJaieq7AeOYT2/Ahrw0fduwZa5O0JHlLkuuS7E7yR0k+UxTFS0qlUvVI\nFyiVSh/KgcdOfuyGG25Yfdg1oWHKlcH0dq0zYLLGqtVq/vbuXbn+8o1pbfVnCwAAS1E9Z2CMHPy6\n+pDX1hx27PBz/7hUKj1WKpXGcmB+xotzYBUHLEoHdiAx/6LW7n+snMd3V/JTl57U6CgAAMACqVuB\nUSqVhpM8kQMlxDNekgNlxWOHnbs3yeNJjrjSAharcmUgfd3mX9Ta3969K6+4cG3Wr+xodBQAAGCB\n1HuI50eS/E5RFFuTTCZ5b5KPHmGAZ5J8MMm/LoriK0n25MBQz3tKpdL2eoWFWiuPW4FRa/vGpvLV\n+/rzh798fqOjAAAAC6jeBcbvJdmQZFsOrP74VJLfTpKiKD6YJKVS6caD5/5+DszCuPfgubcn+Zk6\n54WaKlcGcuq6CxodY0n50r17sn5VRy49Z/XxTwYAABatuhYYpVJpKsm7D/46/NiNh30/kwPlxm/X\nJx0svNHxwfR1r290jCWjWq3mprt3582GdwIAwJJXzyGesOwdGOKpwKiVB7aX8+iuccM7AQBgGVBg\nQJ3MzExndGJIgVFDN921K9dsWZsNqwzvBACApU6BAXUyNjGcanUmvd2GeNZCeXwqX7lvIG++fGOj\nowAAAHWgwIA6KVcGkyR9nQqMWvjSvf1Z17cil51reCcAACwHCgyok3KlP90dq9PWtqLRURa9arWa\nz9y1K9dffrLhnQAAsEwoMKBOynYgqZlt28t5dNdY3njpyY2OAgAA1IkCA+rkwA4kHh+phU/fuSvX\nbFmXk1Yb3gkAAMuFAgPqZLQyaAeSGhgencwt9/XnLS/f1OgoAABAHSkwoE7K4/0eIamBL3xrTzav\n7cyl56xqdBQAAKCOFBhQJyOVASswTtDMTDWfvmtnfvbKTWlpMbwTAACWEwUG1MloxRDPE3X3D4Yz\nMDKZn3zpSY2OAgAA1JkCA+qgWq2mPD6QXkM8T8in7tyV616yISu72xsdBQAAqDMFBtTB/qnxTE5X\nsrJrQ6OjLFpPD1ZyR2koP2t4JwAALEsKDKiDcqU/SazAOAF/e9euXHh6X849tbfRUQAAgAZQYEAd\nlMcH097Wmc4VPnzPx/6pmXz2m7vzs1dafQEAAMuVAgPqoFwZSF/XOjtnzNPff3cgSfKqFxqCCgAA\ny5UCA+pgtDKQPvMv5u2Td+zM9ZednM4V/pMFAADLlU8DUAfl8YH0dZt/MR8/eHI0D2wv56ev2Njo\nKAAAQAMpMKAOypXB9HV5/GE+Pn3XzlxZrM0p67oaHQUAAGggBQbUQbkykL5uBcZcjYxP5Uv39udn\nX271BQAALHcKDKiD8vhAeq3AmLMvfntP1vWtyOXnrWl0FAAAoMEUGFAH5cpAViow5qRareZTd+zM\nW16+Ma2tdm8BAIDlToEBC2x6ZipjE8Pp7TLEcy7+4Yf7smNoIm982cmNjgIAADQBBQYssNHKUJKY\ngTFHf711R1538Yas6V3R6CgAAEATUGDAAitXBpK0pLfTHIfZ2r5nPLeXhvK2azY3OgoAANAkFBiw\nwEYrA+ntWpvW1vZGR1k0/nrrjlxy1qqce0pvo6MAAABNQoEBC2xkfCB95l/M2r6xqXz+H/bk515x\nSqOjAAAATUSBAQtstDKYPjuQzNpnv7krJ63qyJXne+QGAAD4RwoMWGDl8f70KjBmZWq6mr+5fWfe\ndvVmW6cCAADPosCABVauDNqBZJa+/r2BjE1M5w2XntToKAAAQJNRYMACK1cGstIKjFn5v7ftyJsv\n35iezrZGRwEAAJqMAgMWWHl8IL3dhngez/ceG0npyXLeeuWmRkcBAACakAIDFlC1Wj3wCIkVGMf1\nidt25FUvXJ+NazsbHQUAAGhCCgxYQJXJkUzP7FdgHMfOoYl8/f6B/Nw1mxsdBQAAaFIKDFhA5fGB\nJDHE8zj+5vYdueC0vlx4xspGRwEAAJqUAgMW0Mh4fzpX9KWjvbvRUZrW2MR0brp7d37uFac0OgoA\nANDEFBiwgMrj/VnZvaHRMZra57+1O33dbbn2QoNOAQCAo1NgwAIaqfR7fOQYZmaq+eutO/LWKzen\nva2l0XEAAIAmpsCABVQeH8jKLiswjub2B4cyMDKZ6y8/udFRAACAJnfcAqMoircWRdFRjzCw1IyM\nW4FxLH9569N502UnZ2V3e6OjAAAATW42KzD+b5I1z3xTFEWpKIrTFy4SLB1mYBzddx7ZlwceL+cX\nDO8EAABmYTYFxuEPpj8vib8uhVkYqQykT4FxRB/92lP5yUs2ZNPazkZHAQAAFgEzMGCBVKvVAysw\nzMB4ju8/Wc43fzCcf/qqUxsdBQAAWCRmU2BUD/46/DXgGMb378v0zKQVGEfw0a89lVe+cH3OOLm7\n0VEAAIBFYjaPgrQk+WRRFPsPft+V5GNFUYwfelKpVHpdrcPBYlYe70+SrDTE81ke2zWeW+8fzF/8\nxgsbHQUAAFhEZlNg/MVh3398IYLAUjMy3p/ujlVpbzPj4VB/+fWncvl5a3Le83obHQUAAFhEjltg\nlEqlf16PILDUjIz3p6/L6otD7RyayBfv6c+f/csLGh0FAABYZOY8xLMoig1FUfhUBsdRrvSbf3GY\nj9/6dC46sy8vfsGqRkcBAAAWmVlth1oUxUlJ/muSn0my6uBre5N8Osl7SqXSngVLCIvUyHh/Viow\nfmxwZDKfvXtXfv+fn9foKAAAwCJ03AKjKIqeJFuTnJTkL5Nsy4HBnhcm+fkkVxZFcUmpVBo/+rvA\n8lMeH8j6VWc0OkbT+MTWHTlzY08uP29No6MAAACL0GxWYLwrSXeSi0ql0tOHHiiK4v9NcmeSX0vy\nvtrHg8WrPN6fMzde0ugYTWFkfCqfvGNn/sNbz0pLS0uj4wAAAIvQbGZgvCnJ7x1eXiRJqVR6Kgce\nLbm+1sFgsRupeITkGZ+6Y2fWr1yRV160rtFRAACARWo2Bcb5SW4/xvGtSYraxIGloVqdSXl8wC4k\nSSr7p/N/b9uRX3rVqWlrtfoCAACYn9kUGKuTDBzj+MDBc4CDxib2ZqY6bReSJJ/95u50rmjNdS/x\nZwEAAMzfbAqMtiTTxzg+c/Ac4KCR8f4kSV/X8n5kYmJyJn/59afz9mtPyYr2Oe/aDAAA8GOzGeLZ\nkuSTRVHsP8rxjhrmgSWhPN6fns41aW9b3rfHp+/cmSR58+UbG5wEAABY7GZTYHwsSfU45zxagyyw\nZIxUzL8Ym5jOX/z9U/mXrz89nSusvgAAAE7MbAqMX0myJcmPSqXS2KEHiqLoSXJ2kgcWIBssWuVx\nO5D89dYd6e1sy09delKjowAAAEvAbP5a9OdzYBXGxBGO7T947B21DAWL3ch4/7Ie4DkyPpWP3/p0\n3vkTp6W9zeoLAADgxM3mk8U7kryvVCo9Z5BnqVSaSvKHSX6h1sFgMVvuKzA+fuvT2bCqI6+9ePn+\nGQAAALU1mwLjvCR3HuP4XQfPAQ4aGe9ftjMwBkcm84nbduTG605LW2tLo+MAAABLxGwKjNVJVhzj\neEeSVbWJA0tDubJ8V2B87OtP5YyTu3PtRct7C1kAAKC2ZlNgPJ7kxcc4/uIk22sTBxa/mZnplCuD\ny3IGxu69E/nUHTtz43WnpaXF6gsAAKB2ZlNgfC7J7xZF0Xf4gaIoViX5TwfPAZKMTgylWp1Zlisw\n/vyWp1I8ry9XnL+m0VEAAIAlZjbbqP7XJDckeagoivcnKR18/YIk/yrJZJL/tjDxYPEpjw8kaUlv\n1/J6hOKpgUo++83d+dMbC6svAACAmjtugVEqlQaLorgyyZ8l+d3846qNmSQ3J/m1Uqk0sHARYXEp\nV/rT27U2ba2z6QeXjo985cm89OxVeclZqxsdBQAAWIJm9QmrVCo9meSNRVGsTXJ2kpYkPyyVSkML\nGQ4Wo5Hx/qzsWl6Pjzy6ayxfundPPvLrFzY6CgAAsETN6a+IDxYW/7BAWWBJGBnvT1/38tpC9cNf\nfjJXFmuz5fSVjY4CAAAsUbMZ4gnMQXm8f1ntQLJt+0i+fv9AfvW60xodBQAAWMIUGFBjI+MDy2YH\nkmq1mj/63OP5yZeelHNO6W10HAAAYAlTYECNlcf709e1PB4hufX+wfzgqdHceN3pjY4CAAAscQoM\nqLFyZXmswJicmskH/m57fvHaU3LS6o5GxwEAAJY4BQbU0MzMVMqVwWVRYHzqzl2p7J/O2689pdFR\nAACAZUCBATVUrgwlqS75IZ57xybzv77yRG58/enp7mxrdBwAAGAZUGBADZXH+9PS0prezrWNjrKg\n/vyWp3Lyms785EtPanQUAABgmVBgQA2NVPrT27k2ra1Ld1XCE/3j+eQdO/Mbbzojba0tjY4DAAAs\nEwoMqKHyeP+Sn3/xp3+3PZeduzovO3dNo6MAAADLiAIDamhkvH9Jz7+475F9+cYDg/n1nzqj0VEA\nAIBlRoEBNTSyhFdgzMxU88effzzXX7YxL9jU0+g4AADAMqPAgBoqVwaW7AqMW+4byGO7xvPOnzit\n0VEAAIBlSIEBNVQe78/KrqVXYExMzuR/fnF7funVp2bdyhWNjgMAACxDCgyooaX6CMlffePpJMnb\nrtnU4CQAAMBypcCAGpmenszYxHD6utc3OkpN7d47kY9+7am8+41npGvF0t0eFgAAaG4KDKiRcmUg\nSZbcDIwPfGF7LjitL6964bpGRwEAAJYxBQbUyMh4f1pa2tLbuabRUWrme4+O5Jb7+vNv3nxmWlpa\nGh0HAABYxhQYUCPlykD6utanpWVp3FYzM9X84U2P5vrLNubcU3obHQcAAFjmlsYnLWgCBwZ4Lp35\nF1/4hz15aqCSG6+zbSoAANB4CgyokfIS2oGkXJnK//zi9rzzJ07Lmj7bpgIAAI2nwIAaGRkfWDID\nPP/8liezurc9b3n5xkZHAQAASKLAgJopV/rT17X4HyF5fM94PrF1Z/7N9Wemvc1/IgAAgObg0wnU\nyMgSeYTkjz77WF5+/ppcdt7S2U0FAABY/BQYUCNLYQbGnaWhfOuhvfmNN53Z6CgAAADPosCAGpia\nnsj4/n2LegbG5NRM/sdnH8vPv2Jznrehq9FxAAAAnkWBATUwMj6QJIt6Bcan7tyVcmU6/+zVz2t0\nFAAAgOdQYEANlCsDaW1tT3fH6kZHmZfh0cl85CtP5Nd+8vT0drU1Og4AAMBzKDCgBsrj/VnZtSEt\nLS2NjjIvH/7ykzllXVfe8NKTGh0FAADgiBQYUAMj4/3p616cW6g+snMsn7lrZ37z+jPT2ro4CxgA\nAGDpU2BADSzmHUj+5POP5xUXrstLzlrV6CgAAABHpcCAGhgZ709f1+IrMO76/lD+4Yd78+s/dUaj\nowAAAByTAgNqYKSy+FZgTE1X80efezxvu2ZzTl1v21QAAKC5KTCgBsrjA+lbZAXGTXfvyvDoZP75\na05tdBQAAIDjUmBADYwsshkY+8am8v996Yn86nWnp6+rvdFxAAAAjkuBASdocqqSiclyVi6iXUj+\n/KtP5qTVHXnTy05udBQAAIBZUWDACRqpDCTJohniuX3PeP7m9p35jTedkfY226YCAACLgwIDTlB5\nvD9trR3p6ljZ6Ciz8ieffzxXnLcmLzt3TaOjAAAAzJoCA07QgfkX69PS0vyrGb79o725ozScd7/J\ntqkAAMDiUtfpfUVRtCd5X5JfzIHy5NNJ3lUqlSrH+JnuJPcn2VQqlfrqEhTmoDzevyh2IKlWq/nA\nFx7PW16+MWec1N3oOAAAAHNS7xUY70nyyiQXJTknyQVJfv84P/Ofkzy+wLlg3hbLDiS3bRvKI7vG\nbZsKAAAsSvUuMN6R5PdKpdJTpVJpT5L3JvlnRVG0HenkoiguSXJdkv9Wv4gwNyPj/VnZ5AM8p2eq\n+eDN2/O2qzdn/cqORscBAACYs7o9QlIUxZokpyW575CX702yMsmZSR4+7Pz2JB9O8q7MsmgpiuKd\nSd556GttbW1tW7ZsmXduOJ5yZSAb15zd6BjHdMt9/dk1vD9vv/aURkcBAACYl3rOwHhmi4bhQ14b\nPuzYof5dku+USqXbiqK4djYXKJVKH0ryoUNfu+GGG1Yfdk2oqQMzMNY3OsZRTU3P5ENfeiK/cO0p\nWdVT17E3AAAANVPPTzMjB7+uTrLz4O/XHHYsSVIUxdlJbkxycX2iwfw1+wyML/zDnoxOTOdtV29u\ndBQAAIB5q9sMjFKpNJzkiSQvPuTll+RAefHYYadflWRjkoeKouhP8tkkvUVR9BdFcU0d4sKsTEyO\nZf/UWNMWGBOTM/nIV57ML73q1PR2HXHUDAAAwKJQ7/XkH0nyO0VRbE0ymQNDPD9aKpWmDzvvb5J8\n9ZDvr0jy0RwoP/YsfEyYnXKlP0nS16RDPD9z164kyVtevqnBSQAAAE5MvQuM30uyIcm2HFj98akk\nv50kRVF8MElKpdKNpVJpLMnYMz9UFMWeJNVSqfRknfPCMY2M92dFW1c6V/Q2OspzjE1M56NfezI3\nXnd6OlfUe8MhAACA2qprgVEqlaaSvPvgr8OP3XiMn7s1Sd/CJYP5OTDAc0NaWloaHeU5PrF1R3o7\n2/LGl53U6CgAAAAnzF/Lwglo1gGe+8am8vGvP513/sRpaW9zmwMAAIufTzZwAsrjA+nrar4tVD9+\n69PZuKYjr724+coVAACA+VBgwAkYqTTfCoyBkf35xNYd+dXrTk9ba/M92gIAADAfCgw4AeXxgfR1\nN9cKjL/4+6fzgo3decWFaxsdBQAAoGYUGHACmm0GxvDoZG66e1d+5XWnNeVgUQAAgPlSYMA8VavV\nH+9C0iw+feeubF7bmSvPX9PoKAAAADWlwIB5mpgczeR0pWlWYExMzuSTt+/ML7zilLSafQEAACwx\nCgyYp3KlP0nS19UcBcYXv70nra3JdZc0Rx4AAIBaUmDAPI2M96ejvTudK3oaHSUzM9X81Teezg1X\nbU5Hu9saAABYenzSgXk6sANJc6x2uG3bUPr37c/PvHxjo6MAAAAsCAUGzFMz7UDy8VufzvWXbczK\n7vZGRwEAAFgQCgyYp/J4f1PMv/jeoyPZtn0kP3fN5kZHAQAAWDAKDJinkUpzrMD4y1ufymtfvCGb\n1nY2OgoAAMCCUWDAPDXDIySP7xnPbduG8vZXntLQHAAAAAtNgQHzVB7vb/gQz/9z69N52Tmrc+4p\nvQ3NAQAAsNAUGDAP1Wo15cpA+rrWNSzDwMj+/N239+Tt11p9AQAALH0KDJiHicnRTE3vT1/3+oZl\n+OTtO3Pmyd152bmrG5YBAACgXhQYMA/lykCSNGwXkvGJ6Xzqzl15+7WnpKWlpSEZAAAA6kmBAfNQ\nrgxkRVtXOtq7G3L9z31rd7o7WvOaFzduBQgAAEA9KTBgHkYrg+ntWteQ1Q8zM9X89dYdeds1m9Pe\n5hYGAACWB59+YB7K4wMNm3/x7R/tzZ69+/PGS09uyPUBAAAaQYEB83BgB5LGFBg33b07r3rR+qzq\naW/I9QEAABpBgQHzUB5vzBaqQ+XJ3PrAYK6/bGPdrw0AANBICgyYh3JlsCGPkHzx23ty6rrOXPyC\nlXW/NgAAQCMpMGAeDgzxrG+BUa1Wc9Pdu3L9ZRttnQoAACw7CgyYh3JlICvrXGDc9+hInhqcyBsu\nPamu1wUAAGgGCgyYo+mZqYxNDKe3zjMwbrp7V669cF3W9q2o63UBAACagQID5mi0MpQkdZ2BsW9s\nKn//3YG8+XJbpwIAAMuTAgPmqFwZSNKS3s41dbvml+7dk/WrOvLSs1fX7ZoAAADNRIEBczRaGUhv\n55q0trbX5XoHhnfuzvWXnZzWVsM7AQCA5UmBAXNUHh+s6/yLbdvLeXTXWN74Mo+PAAAAy5cCA+ao\nXBmo6/yLm+7enSuLtdmwqqNu1wQAAGg2CgyYo3JlIH112kJ1tDKdW+7rz09fsbEu1wMAAGhWCgyY\no/J4/VZgfOU7/VnV057Lz6vfwFAAAIBmpMCAORqtDKavTjMwbvrmrrzxZSenzfBOAABgmVNgwByV\nKwPprcMjJA89NZrvPzmaNxneCQAAoMCAuahWq3WbgXHT3btyxXlrsmlt54JfCwAAoNkpMGAOJiZH\nMzW9P33dC/sISWVyOl+6tz/XX271BQAAQKLAgDkZrQwmyYKvwLizNJzWluSqYu2CXgcAAGCxUGDA\nHIxU+rOirSsd7T0Lep2vfKc/r3rR+qxod4sCAAAkCgyYk9HKYHq71qWlZeF2BSlXpnLHg0N53cUb\nFuwaAAAAi40CA+agHluo3vbAUFb2tOfiF6xa0OsAAAAsJgoMmIPRylB6uxZ2LsUt9/XnNS9an7bW\nhVvlAQAAsNgoMGAORieG09O5cAXG8Ohk7v7BXo+PAAAAHEaBAXNwYAbGwhUYf/+9wWxc05Etp/ct\n2DUAAAAWIwUGzMFYZSi9C7gC45bv9Od1F29Y0CGhAAAAi5ECA+ZgdGIoPQu0AmP33onc+8i+vO7i\n9Qvy/gAAAIuZAgPmYHRieMEeIfnqfQM58+TunLWpZ0HeHwAAYDFTYMAs7Z8az+TU+II9QnLLfQMe\nHwEAADgKBQbM0tjEcJIsyAqMJ/sr2ba97PERAACAo1BgwCyNVobS0tKa7o5VNX/vr9zXnwtO681p\nG7pr/t4AAABLgQIDZmm0MpiezjVpaan9bXPLd/rz2os31Px9AQAAlgoFBszS6MTwgsy/eHjHWB7Z\nNZ7XvtjjIwAAAEejwIBZGqsMpadrTc3f98vf6c/FL1iVk1d31vy9AQAAlgoFBszS6MRQejvX1fQ9\nq9Vqbrmv3+oLAACA41BgwCyNVoZqvgPJg0+Us3Nof179QgUGAADAsSgwYJbGJoZqPgPjy98ZyGXn\nrs6avhU1fV8AAIClRoEBszRaGUpPDVdgTM9U89X77D4CAAAwGwoMmKUDMzBqN8Tzu4+OZN/YVF5x\nYe13NgEAAFhqFBgwC9XqTMYmhms6A+MbDwzmZeeuSV9Xe83eEwAAYKlSYMAsjO/fl2p1Jj01moFR\nrVZz6wODVl8AAADMkgIDZmG0MpQkNVuB8cOnx7JraCLXbKnttqwAAABLlQIDZmF0Yigr2rvT0d5d\nk/e79YHBvPD5K7PW7iMAAACzosCAWRir1HaA5633D+baC62+AAAAmC0FBszC6MRwems0/+KpgUp+\ntGMsr1BgAAAAzJoCA2ZhtDKYnhrNv7j1gcGcc0pPTl3fVZP3AwAAWA4UGDALo5Whmq3A+MYDg1Zf\nAAAAzJECA2ZhbGKoJiswBkcm891HR3LtRQoMAACAuVBgwCyMVobTV4MCY+uDg9m0tjPnbO6pQSoA\nAIDlQ4EBszA6MZSeGjxC8szuIy0tLTVIBQAAsHwoMGAWxiaG0nuCKzBGK9P51kN7zb8AAACYBwUG\nHMfU9EQmJkdPeIjnXT8YTm9XW170/JU1SgYAALB8KDDgOEYrQ0lywkM8b71/IFdvWZu2Vo+PAAAA\nzJUCA45jdGI4SUt6OlbN+z0mp2ZyR2k41160vnbBAAAAlhEFBhzHWGUoPZ2r09raPu/3+PaP9mVm\nppqXnbO6hskAAACWDwUGHMfoxNAJz7/4xgODueL8Nelc4ZYDAACYD5+m4DhGK0MnNP9iZqaa27Yd\n2D4VAACA+VFgwHGMneAKjG1PlDNUnsqVF5zYKg4AAIDlTIEBxzFaGUrvCazAuPX+wbz07FVZ2T3/\nGRoAAADLnQIDjmN0Yig9nWvm9bPVajW33j+YV1zk8REAAIATocCA4ziRFRiP7hrPE/2VXLPF4yMA\nAAAnQoEBx3EiMzBu2zaULaf35eTVnTVOBQAAsLwoMOAYqtXqwRUY83sEZOu2wVxleCcAAMAJU2DA\nMVQmRzJTnZ7XNqqDI5N5YHs5V3t8BAAA4IQpMOAYxirDSTKvR0juLA3l5NUdOWdzT61jAQAALDsK\nDDiG0YmhtLd1pKO9e84/u/XBoVx9wdq0tLQsQDIAAIDlRYEBxzBaGUxP59xLiP1TM/nmQ8MeHwEA\nAKgRBQYcw2hlfjuQ3POjfalWk5ectXoBUgEAACw/Cgw4htGJofTOY4Dn7Q8O5bLz1qRzhVsMAACg\nFny6gmMYqwylZ44rMKrVam5/cChXFR4fAQAAqBUFBhzD6MRw+ua4AuPhnWPZOTyRKy9Ys0CpAAAA\nlh8FBhzD6MRQeuZYYGzdNpQtp/Vl/cqOBUoFAACw/Cgw4BjG5jHEc+uDQ7nK7iMAAAA1pcCAYxit\nDM5pBcbgyGS2bS/n6gsUGAAAALWkwICjmJ6eTGWyPKcVGHeUhrJxTUfO3tyzgMkAAACWHwUGHMXo\nxHCSzGkb1a0PDuXqC9ampaVloWIBAAAsSwoMOIqxiaEkSU/n7HYT2T81k2/+YDhXXbBuIWMBAAAs\nSwoMOIrRylC6O1alrbV9Vuff86N9SZJLzl61kLEAAACWJQUGHMXoxFB65jD/4vYHB3PZeWvS0e62\nAgAAqDWftOAoxipDs55/Ua1Wfzz/AgAAgNpTYMBRjE4MpXeW8y9+tGMsu4b358pCgQEAALAQFBhw\nFKOVofTMcgXG1geHsuX0vqxbuWKBUwEAACxPCgw4igMrMGZXYNy+zeMjAAAAC0mBAUcx2xkYAyP7\ns+2Jcq7eosAAAABYKAoMOIrRiaH0dq077nl3loazaU1nztrUU4dUAAAAy5MCA46gWq1mtDKcnlkM\n8dz64FCuumBtWlpa6pAMAABgeWqv58WKomhP8r4kv5gD5cmnk7yrVCpVDjuvM8kHkrw6yUlJ5o48\ncwAAIABJREFUdiR5f6lUen8987J87Z8ay/TM/uM+QjIxOZNv/mA4/+2fnVenZAAAAMtTvVdgvCfJ\nK5NclOScJBck+f0jnNeeZGeS1yVZneStSf5jURRvrVNOlrnRymCSHHeI570P701LS/KSs1bVIxYA\nAMCyVdcVGEnekeS3SqXSU0lSFMV7k3yyKIrfLJVK08+cVCqVRpP8P4f83H1FUXwuyVVJ/qaOeVmm\nRitDaW1tT+eKvmOet3XbUC47d0062j2NBQAAsJDqVmAURbEmyWlJ7jvk5XuTrExyZpKHj/GzK5Jc\nneQPj3ONdyZ556GvtbW1tW3ZsmV+oVm2ntlC9VhzLarVarY+OJRf/YnT6pgMAABgearnCoyVB78O\nH/La8GHHjuYDSUaSfOxYJ5VKpQ8l+dChr91www2rD7smHNdoZei4j4/8cMdYdu/dn5cXtk8FAABY\naPVc9z5y8OvqQ15bc9ix5yiK4r8nuSLJ60ul0v4FygbPMjYxfNwBnlu3DeXC0/uybuWKOqUCAABY\nvupWYJRKpeEkTyR58SEvvyQHyovHjvQzRVH8UZLXJnl1qVTqX+iM8IzRylB6jlNg3PHgUK7esq5O\niQAAAJa3ek8e/EiS3ymK4pSiKE5K8t4kHz10gOcziqL4kySvSfKqUqm0p74xWe7GJo79CMnAyP5s\ne6Kcqy/w+AgAAEA91HsXkt9LsiHJthwoTz6V5LeTpCiKDyZJqVS6sSiKM5L8epKJJI8WRfHMz28t\nlUqvr3NmlqFyZTCb151/1ON3PDicTWs784JN3XVMBQAAsHzVtcAolUpTSd598Nfhx2485PePJzn6\n9g+wwMYmhtNzjBUYWx8czNUXHHuXEgAAAGqn3o+QwKIwWhlKb9eaIx6bmJzJNx/a6/ERAACAOlJg\nwGFmZqYyvn/vUWdg3POjvWltSS4+a1WdkwEAACxfCgw4zNjE3iRJb9eRdxjZ+uBQLj9vTTra3T4A\nAAD14hMYHGZ0YihJ0tP53EdIqtVqbn9wKFd5fAQAAKCuFBhwmNHKUDpX9KW9reM5x364Yyy79+7P\nlYUCAwAAoJ4UGHCY0cpQeo+w+iJJtm4bykVnrMzavhV1TgUAALC8KTDgMGMTQ+npOvIKi9sfHLL7\nCAAAQAMoMOAwoxNDR9yBpH/f/mzbXs5VWxQYAAAA9abAgMOMVobSe4QVGHeUhnLKus68YGN3A1IB\nAAAsbwoMOMzYxNARt1Ddum0oV29Zm5aWlgakAgAAWN4UGHCY0crQc7ZQnZicybd+uDdX2X0EAACg\nIRQYcJhyZSB9Xeuf9dq3f7Q3bS0teclZqxqUCgAAYHlTYMAhqtVqypXB9B32CMntDw7lsvNWZ0W7\nWwYAAKARfBqDQ1T2j2RmZiq93f+4AmNmpprbHhjMNVueOxcDAACA+lBgwCHKlf4kedYKjG1PlDNY\nnsrVtk8FAABoGAUGHKJcGUxHe0862v9xq9Svf28wl56zKiu72xuYDAAAYHlTYMAhyuPPnn9RrVbz\n9fsH8sqL1h/jpwAAAFhoCgw4RLnSn77uDT/+/oc7xvL04ESuudDjIwAAAI2kwIBDjB62A8mt3xvM\ni56/MutXdjQwFQAAAAoMOES5MpjeQwqMr98/kFd5fAQAAKDhFBhwiPL4QPoObqH6+J7xPLxzPNde\nZPtUAACARlNgwCHKlYH0dR0oMG69fzDFab3ZtLazwakAAABQYMAhDp2Bcev9g3YfAQAAaBIKDDho\nanp/xvfvS2/X+uwamsi27eW80uMjAAAATUGBAQeNVgaTJH3d63PrA4N5/sbunHFyd4NTAQAAkCgw\n4MfKlcG0tLSmp2N1vn7/oNUXAAAATUSBAQeVKwPp7Vyb4dGZ3PfIPvMvAAAAmogCAw4qjx/YgeS2\nbYPZtLYz557a0+hIAAAAHKTAgINGK4Pp7V53cPeRdWlpaWl0JAAAAA5SYMBB5cpAOtvX5ls/3Ovx\nEQAAgCajwICDypXBDJZ7srqnPRee0dfoOAAAABxCgQEHjY4P5PHdnbn2onVpbfX4CAAAQDNRYMBB\n+8YH8v0n222fCgAA0IQUGJCkWq2mXBnIzMyqvOSsVY2OAwAAwGEUGJCkMjmSanUqL3z+aWlvc1sA\nAAA0G5/UIMngSH+S5JoLzmxsEAAAAI5IgQFJ7v7BY5ma7sxVWzY3OgoAAABHoMCAJN/+4aNpb1uX\nFe1uCQAAgGbk0xrL3lB5Mk8O7MhJqzc1OgoAAABHocBg2bvlvv6sXzmSU9ad0ugoAAAAHIUCg2Xv\n5nv6c8q60azqObnRUQAAADgKBQbL2uO7x7Ntezk9ncNZ1bOx0XEAAAA4CgUGy9rN9+zJC89cmfH9\n/VZgAAAANDEFBsvWzEw1N9/bn9dd3JuJyXJWKzAAAACalgKDZeu7j42kf+/+XHrOVJJkVbcCAwAA\noFkpMFi2br5nT668YG1mZgbTtaIvHSt6Gh0JAACAo1BgsCxNTM7kq/cN5PWXbMi+sd0GeAIAADQ5\nBQbL0u0PDqW1JbmyWJt947sN8AQAAGhyCgyWpZvv2ZPXvHhDOtpbs29slwIDAACgySkwWHaGy5O5\nozScn7zkpCQ5+AiJAgMAAKCZKTBYdm65byCb13bkojP7khwsMOxAAgAA0NQUGCw7N9+zJ9ddclJa\nWlpSrVYN8QQAAFgEFBgsK9v3jOeB7eW8/pINSZLK/pFMTlc8QgIAANDkFBgsK1+6pz8XndGX0zZ0\nJ0n2ju1KkqzqOamRsQAAADgOBQbLRrVazc337snrX/qPZcW+sV3p6VyT9rbOBiYDAADgeBQYLBvf\nemhv+vfuz2tfvP7Hr9mBBAAAYHFQYLBsfGLrjrz+pSdldc+KH7+2b9wATwAAgMVAgcGy8Pju8dz5\n/eHccPXmZ71uC1UAAIDFQYHBsvDXW3fkZeeszlmbep71+t7RnVndawUGAABAs1NgsOTtG5vKF769\nJ2+7ZvNzjg2Wn8y6lac1IBUAAABzocBgybvp7l3ZuLojV5y35lmvT0yOZrQymPUKDAAAgKanwGBJ\nm5qeyd/cvjM3XLM5ra0tzzo2OPJEWlpas6b3lAalAwAAYLYUGCxpX79/MOP7p/OGS056zrGBkSey\numdj2ts6GpAMAACAuVBgsKR94rYdefPlG9Pd2facY4MjT5h/AQAAsEgoMFiyHnh8JA8+Uc4/uXLT\nEY8Plp/Muj4FBgAAwGKgwGDJ+sTWHXnlReuzaW3nEY8PjjxhgCcAAMAiocBgSdo1PJGvfXfwiFun\nJkm1Ws2AR0gAAAAWDQUGS9Kn79yV85/Xm4vO6Dvi8bGJ4UxMlrNu5fPqnAwAAID5UGCw5FT2T+cz\nd+3K267enJaWliOeMzjyRFpb27O658jzMQAAAGguCgyWnJvv6U/Xita8+kXrjnrOwMgTWdt7alpb\nn7s7CQAAAM1HgcGSUq1W84mtO/JPrtyU9raj/+s9WH7SAE8AAIBFRIHBkrJ121CeHpzI9ZeffMzz\nBg3wBAAAWFQUGCwZU9Mzef8XHs/PXbM5a3pXHPPcAwWGAZ4AAACLhQKDJeMzd+3KyPh0fulVpx7z\nvGp1JoPlJ63AAAAAWEQUGCwJ+8am8uEvP5kbX39aeruOPZhz3/ieTE1PZP3K0+uUDgAAgBOlwGBJ\n+N9ffTInre7IG1927NkXSbJ7+OF0rehLX9f6OiQDAACgFhQYLHpP9lfy17fvzL9+4xlpa2057vk7\nBr+fzevOT0vL8c8FAACgOSgwWPQ+8HeP57JzV+ey89bM6vynB7+fzeuKBU4FAABALSkwWNS+88i+\nfOOBwbz7jWfM6vxqtZodg6Wcsu78BU4GAABALSkwWLRmZqr5o889lp+5YmOev7FnVj+zb2xXRieG\nslmBAQAAsKgoMFi0vvyd/jyxp5J3vG7226E+Pfj99Haty6ru4w/7BAAAoHkoMFiUKvun86d/tz2/\n/JrnZW3filn/3DOPjxjgCQAAsLgoMFiU/uobO9Le1pK3Xr1pTj9ngCcAAMDipMBg0enftz8f+/un\n8us/dUY62mf/r3C1OpMdQ983wBMAAGARUmCwqMzMVPNf/ubhnPe83rzqhevm9LOD5SczMTmaU6zA\nAAAAWHQUGCwqH/37p/LA4+X8558/Z85zLJ4e+H5W925OT+eaBUoHAADAQmlvdACYrW89NJwPf/nJ\n/PdfOT+b1nbO+eefGeAJAADA4mMFBovCruGJ/MeP/zC//JpTc8X581tB8ciuf8jpG15U42QAAADU\ngwKDpjc5NZP3fOyhFKf15Vde+7x5vcfgyBPp3/dYzjn1qhqnAwAAoB4UGDS9P/nC49mzb3/+08+f\nndbWuc29eMYPntqaTWvOzZreuW27CgAAQHNQYNDUbvlOfz5z56781396Xtb0rpj3+zz01O051+oL\nAACARUuBQdN6dNdY/ssnH85vvvnMXHB637zfZ7QylCf678+5p15dw3QAAADUkwKDpjQ2MZ1//xcP\n5RUXrstbrth4Qu/1w6fvyOqejdm45uwapQMAAKDeFBg0nfGJ6bznYw+lpSX59295QVpa5jf34hnP\nPD5you8DAABA4ygwaCqDI5P5tQ8+mJ3DE/njdxTp7mw7offbPzmWR3Z9y+MjAAAAi5wCg6bxRP94\n3vH++9O5ojUf/lcXZuPazhN+z+888vn0dK7N6Se9qAYJAQAAaBQFBk3hgcdH8it/8kDOf15f/uSd\nRVZ2t5/we05NT+Su7/+fXFm8PW2tJ/5+AAAANI5PdTTc1m2Dec9f/jBvefnGvPunzkhra21mVXz3\n0S8mSV70/J+syfsBAADQOAoMGuozd+3KH3zm0bz7jWfk567ZXLP3nZ6ezB2lj+eK838h7W0n/igK\nAAAAjaXAoCHGJ6bzoS8/kU/esTO/+/Zz8poXra/p+3/3sZszNT2Rl5z1ppq+LwAAAI2hwKCuZmaq\n+eI9e/JnX9yero62fOBXL8iLX7CqptcYHHkiX/vun+aVF/1qVrR31fS9AQAAaAwFBnVz78P78kef\neyxP9lfyK697Xt565aasaK/tHNnJqUo+dcd/zFmbLsslZ/90Td8bAACAxlFgsOCe7K/k/V94PLdt\nG8zPXLExf/IviqzpW1Hz61Sr1Xzx23+Q6ZmpvOHSf5+WltoMAwUAAKDxFBgsiGq1mod3juVz39qT\nT9+xM5eeszp/9W9flBds6lmQ601OTeTz3/q9PLLzm/mlV/9ZOlcszHUAAABoDAUGNfXIzrF89bsD\n+ep9A3l8z3he/PyV+cNfPj9XnL9mwa65d2xXPnX7f8j+qbH88ms/nHUrT1uwawEAANAYCgxOSLVa\nzeO7K/na9wby1fv68/DO8bzwzJV5y8s35pUvXJeTVy/cFqYTk2O56/t/lbt/8ImccfLF+YXL/0e6\nOlYu2PUAAABonLoWGEVRtCd5X5JfTNKa5NNJ3lUqlSonci71MTU9k8d2j+ehp8byg6dG89DTo/nh\nU6PZNz6dLaf35adednJe/cL12bR24UqLJNk9/HC+++gXc//jX05Xx8r89BXvzbmnXGXmBQAAwBJW\n7xUY70nyyiQXJdmf5HNJfj/Ju0/wXE7Q1HQ1I+NT6d+3/+CvyWd93TE0kYd3jGVyuprnre/Muaf2\n5tJzVucXXnFKzn9ebzas6ligXPszMLI9u4cfyfY99+Wx3fdmqPxUnr/xkrzu4nenOO1VaWu1kAgA\nAGCpq/cnv3ck+a1SqfRUkhRF8d4knyyK4jdLpdL0CZy7pOyfmsnnv7U74xMzqaaamWpSrR54XGPm\n4NfqwddmqtVUc/D3Mwd+PzVdzdT0TKamq5mcrv746+TUTCYmZzK+fyZjE9MZm5jO+P7pjFWmMzFV\n/fH1V3W3Zf2qjmxY1ZENq1bk1PVdueTsVTnnlN6cc0pP+rrm96/N04Pfz/bd38lMdTrTM1OZqU5n\npjqd6syBr5PTExmf2JvRieGMT+zN2MRwRieGUq3OZHXv5py24aK8vHh7XrDp0qzu2VibP2wAAAAW\nhboVGEVRrElyWpL7Dnn53iQrk5yZ5OH5nHvYNd6Z5J2HvtbW1ta2ZcuWE85fT+P7p3PbtqFMTM6k\npSVpbWlJS3Lg962H/L6lJS0tz/59krS3tWRFW2tWtLeka0Vr2rtb097akva2lnR1tKanoy09nW3p\n7mw98LWjLb2dbenrbsv6VSvStaJtQf65dgyW8tDTd6S1tS2tLW1pbWlP28Hft7S2pb21M6t6NmbT\n2nPT07kmPZ1r0te9IRtWnp4Ou4oAAAAsa/VcgfHMdMXhQ14bPuzYfM79sVKp9KEkHzr0tRtuuGH1\nYe/T9Fb3rMgf/4ui0TFq7pKzfzqXnP3TjY4BAADAItRax2uNHPy6+pDX1hx2bD7nAgAAAEtc3QqM\nUqk0nOSJJC8+5OWX5EAh8dh8zwUAAACWvnoP8fxIkt8pimJrkskk703y0aMM5ZzLuQAAAMASVu8C\n4/eSbEiyLQdWf3wqyW8nSVEUH0ySUql04/HOBQAAAJaXuhYYpVJpKsm7D/46/NiNsz0XAAAAWF7q\nOcQTAAAAYF4UGAAAAEDTU2AAAAAATU+BAQAAADQ9BQYAAADQ9BQYAAAAQNNTYAAAAABNT4EBAAAA\nND0FBgAAAND0FBgAAABA01NgAAAAAE1PgQEAAAA0PQUGAAAA0PQUGAAAAEDTU2AAAAAATU+BAQAA\nADQ9BQYAAADQ9BQYAAAAQNNTYAAAAABNT4EBAAAAND0FBgAAAND02hsdoF7GxsYaHQEAAADI/D6j\nL4cCY1WSvOtd72p0DgAAAODZViXZO5sTl0OB8WSS05Psa3QQFta2bdtu3bJly7WNzgGLhXsG5sY9\nA3PjnoHZW8b3y6oc+Mw+Ky3VanUBs0D9FEXx7VKp9NJG54DFwj0Dc+Oegblxz8DsuV9mxxBPAAAA\noOkpMAAAAICmp8AAAAAAmp4Cg6XkQ40OAIuMewbmxj0Dc+Oegdlzv8yCIZ4AAABA07MCAwAAAGh6\nCgwAAACg6SkwAAAAgKanwAAAAACangIDAAAAaHoKDAAAAKDptTc6AJyooijak7wvyS/mQCn36STv\nKpVKlYYGgyZQFEVnkg8keXWSk5LsSPL+Uqn0/oPH3T9wFEVRdCe5P8mmUqnUd/A19wwcQVEUb0jy\nu0nOSzKS5H2lUukP3DPwbEVRbM6B/2/2iiQtSbYm+VelUulJ98vxWYHBUvCeJK9MclGSc5JckOT3\nG5oImkd7kp1JXpdkdZK3JvmPRVG89eBx9w8c3X9O8vhhr7ln4DBFUbwuyYeS/Lsc+N+ac5PcfPCw\newae7X8m6Ujy/CSnJRlN8ucHj7lfjqOlWq02OgOckKIotif5rVKp9ImD3/9Ekk8mWVsqlaYbGg6a\nUFEUH04yXiqV3u3+gSMriuKSJB9N8m+TfOaQFRjuGThMURTfTPK/S6XSB49wzD0DhyiK4ntJ/rBU\nKn3s4PdvSPK/SqXSJvfL8XmEhEWtKIo1OdBc3nfIy/cmWZnkzCQPNyAW/P/t3XnQ1VUdx/E3lk7u\nu2ihmGL40TIDaTI1QdtQlFwqQyeXnFwYRUVxyQWX3MbcMqfFhTEdU8sF3JdCNEsxJEW/8qiBiYLC\nuGWoCD798T03rpfn4bkXkHupz2vmmbv8zu93zv39njP3nu/9nnNblqTlgR2AC9x/zDpWUnh/Awyl\nKlvVfcZsQZJWBvoBd0l6FlgTeBQYBryB+4xZrQuBvSWNBuaR00XG+D2mPp5CYsu6Vcvtm1XPvVmz\nzczmu4ycm3wN7j9mnTkOeCIixtU87z5jtqA1yXn8ewHfJtPiZwA34z5j1pGHgTWA18n+0JucOuL+\nUgcHMGxZ969yu3rVc2vUbDMzQNKFwLbAwIiYg/uP2QIk9QIOJYMYtdxnzBZU+d+/JCKmRsRscjC2\nNRnYAPcZMwAkLQfcDzwOrAasAtwKjAUqC3W6vyyEAxi2TIuIN4GXyDfJij5kJ5/ajDaZtSJJFwPf\nAHaOiFng/mPWie2B7kCbpFnAbcDK5f5WuM+YfUREvEUudtvZwnruM2bzrQX0BC6NiHci4l1ySskW\nwNq4v3TJi3jaMk/SqcCewC7AB8BoYHxEHNnUhpm1CEmXAjsBAyJiZs029x+zKpJWIj9gVmxLLubZ\nG5gJHI/7jNlHSDoBGALsSvaTi4B+EbGN32fMPkrSc8AtwGnkGhgjgGOAT5f77i8L4UU87X/B2cA6\nwNNkVtHvyQ+YZv/3JPUEjgDeB6ZIqmx6KCIG4v5j9hEl/X125bGkmUB7REwrj91nzBZ0PrkWxgSy\nXzxMDsLA7zNmtQaTWRfTyD4xCRgUEe/5PaZrzsAwMzMzMzMzs5bnNTDMzMzMzMzMrOU5gGFmZmZm\nZmZmLc8BDDMzMzMzMzNreQ5gmJmZmZmZmVnLcwDDzMzMzMzMzFqeAxhmZmZmZmZm1vIcwDAzM1vG\nSTpA0tylXGd/Se2SeizNejtpyyhJ9ze7HRWSxkq6otntqJA0UtLzzW6HmZnZ4nIAw8zMbBkiaa6k\nA5rdDuARYAPglWY3ZEmTtH0Jzmzc7LY0YiHtvgD4ShOaZGZmtkR9stkNMDMzs2VPRMwBZjS7Hda1\niHgHeKfZ7TAzM1tcDmCYmZk1QNJY4AVgOvBjYAXgF8ApwMnAUDLD8dcR8ZOq/VYlvwnfE1gNeAo4\nKSLuLds3BqYA3wd+COxEBgjOiIhRpcxU4BPA1ZKuBoiIblV1bAf8HNgcCODQiBhfti0PnAd8D1gX\neB14MCL2WchrPRgYDnwWmA1MAoZExDRJ/YE/ARvWPP5mORfbAFOB4RFxV9Ux1yvt2AVYHXgROC8i\nrirbe5XtOwPtwPhyjKc6a2cH7e4D/BToC6xUzsUpEXF3VZnBwEigNzAHaAMOAd4AHirFpkiinKf+\nndTVE/gVsCMwCzi/gzKLfe1LuWHAgUAvMiAxFjg6IqaXY3TYbkkjgf0iolfVsfYHji/Heg0YBYyM\niLll+1jgefL6DCX/z28HDi8BETMzs6XOU0jMzMwatzewPLA9cAxwEnAHsAqwA3AscJKkgVX7XAV8\nC9gP2Br4M3C7pM1rjn0ucA2wFfA74ApJnyvb+gHzgKPI6RsbVO23HHAOMAzoQw5Kb5RU+bLiCDJ4\nsR+wGbA78NfOXqCkvsAvyzF7kwP0axZ+WoAcqJ8NfBF4FLhB0prlmCsCD5Zt+wICDgf+XbZ3Bx4u\nbd+BnPYwGRgrad066q5YDbgBGECei3uA0ZXzKGl94CbgemBLYFvgYmAu8BIwuBzny+Q53rOjSiR1\nA24B1gb6A7uR57VPTdElce0rjgW+AOwBbFTK0WC7dy1t+i3weTJINRQ4rabo3sBa5bXtAwwigx5m\nZmZN4QwMMzOzxk2JiMpArk3ScKBHRAyseu4YMovgrpJVsDewa0TcU8oMk7QDMAI4qOrYl0XEjQCS\nTiEDDwOAtoiYWb5ZfysiaqdvdAOOiogJZd+RZIBiUzII0JPMMngwItqBf5LZDZ3ZiAws3BoRb5fn\n6smCOL2S6SDpBOAAckB9DzCEzOboFRHTSvkpVfseBkyNiMMqT0g6kszW2JcMMnQpIsbWPHWypN2A\n75KZGRuQAagbI2JqZbeqOl8vd2d2cJ6r7Qx8CegdEW1l3yHkua0ca4lc+/K6LqkqO0XSUGCCpM9E\nxMsNtPsE4A8RcU553FaCOudKOrNMDwJ4MSKOLveflXQD8HUyw8bMzGypcwDDzMyscX+veTyDBdeD\nmAGsV+5vUW7H1ZQZR377X21i5U5EzJP0GtC9jja117SrsrhmdzKAcTVwH/C8pPvK/TFVg9Va9wH/\nIAfK9wF/BG6OiFldtKO6/a9KmlfV/r7AM1XBi1r9gL6SaqcorEhmjdSlZGucTk7FWJ/8vPMpMogD\n8CQZUJlUXttY8rW9VG8dxRbArErwAqAEmSbXlIElcO3LNJ0TyzHXYH4mbU/g5QbavSWZoVLtQfIc\nbcr8YE7t//krZCaJmZlZU3gKiZmZWeM+qHnc3slzi/I+WxtQqPc4H0bEvJr9qOwbERPJ7IdjSx2X\nABMlrdbRwco6B9uQUxXagEPJ4EffBtv/3zbUYTngAXKaRfVfb3K9inqNIqegjCi3W5PBgRUggwPA\nQDLAMR7Yi8xCGNRAHR+HTq+9pI2AO8l1RfYhr83updwKS7s9ZmZmzeAMDDMzs4/f0+X2a+QglKrH\nTzR4rDnkQp4NK0GJW4BbJJ1NLkS6IzCmk/LzyEyBcZJOA54hp4H8bVHqL/sdJKlHJ1kYj5NTTqZF\nxHuLWAfkeR0REaMBJK0MbEIuQgpAmUbzWPk7W9Ld5AKZtzN/4N7VeX4GWEfSZhHxXKlrHTLg8ngp\ns6SufT8yE+WoiHi31FUbTKq33U+X+i+rem5H4F1ygVozM7OW5ACGmZnZxywiXpB0E3C5pEPIX3Y4\njFxAcUiDh5sCDJB0FzCnjikdAEg6jpwCMJH8RZEfkAuCtnVSfjA56B8HzCSnf2xIDtoX1fVkVsRo\nSSPIwfImwDoRcQM5oP4RcJuks8iFKXuQ2RJ3RMQjddYzGdhX0sPkYP4Mqgb1kr5Krl9xLxnE2Yxc\nOPPKUuRF4ENgl7Luw/sR8VYH9TxATrO4VtIRZADhPKqycZbgtX+OzIAYLuk6ciHUU2vK1Nvuc4Ax\nZY2Sm8kMlZHAzxYypcjMzKzpnAZoZma2dBxMrrtwLTno3Q4YFBHPNnic4WQwYSoZWKjX2+QvpvyF\nXIxzD2CviJjcSfk3yF/VuJsMcpwPnBURV3ZSvksRMZv8pn8S+esZQf4E7Ypl+6vkuhCzyIH1ZOA6\nco2H6Q1UdSD5Gecx4NbyGqoXLH2r1HMbGRi4qtRzZlU7TiQXu5xeynX0etqB75TjjSOzN+4EJtQU\nXexrHxFPkot6HkIGkY4lf42muky97b6TXDx0f/JaXARcTq4bYmZm1rK6tbe3d13KzMyvPgeFAAAA\naElEQVTMzMzMzKyJnIFhZmZmZmZmZi3PAQwzMzMzMzMza3kOYJiZmZmZmZlZy3MAw8zMzMzMzMxa\nngMYZmZmZmZmZtbyHMAwMzMzMzMzs5bnAIaZmZmZmZmZtTwHMMzMzMzMzMys5f0HK7+uZd1pI8IA\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4082c7dd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "_ = sns.kdeplot(df[df['class']==0]['months_since_last_donation'],cumulative=True,label='0')\n",
    "_ = sns.kdeplot(df[df['class']==1]['months_since_last_donation'],cumulative=True,label='1')\n",
    "_ = plt.xlabel('months since last donation')\n",
    "_ = plt.ylabel('CDF')\n",
    "_ = plt.title('CDF of recency of donors vs non-donors')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "People who are likely to donate blood again have donated blood more recently. The curve representing the CDF of donors (class 1) grows faster with less recency values than CDF of non-donors (class 0).\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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b9NqCSi1e1yhjWJ5Onl6qo/cboL36hdwuD0APlkol1BitUWOkSg2RSjVFauz+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\nkiSp8v4fYgi0KYiUkBcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f40850ccb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "_ = sns.kdeplot(df[df['class']==0]['num_donations'],cumulative=True,label='0')\n",
    "_ = sns.kdeplot(df[df['class']==1]['num_donations'],cumulative=True,label='1')\n",
    "_ = plt.xlabel('number of donations')\n",
    "_ = plt.ylabel('CDF')\n",
    "_ = plt.title('CDF of frequency of donors vs non-donors')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "From the above plot, we observe that donors donate more frequently than non-donors\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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R6TlsO006nSJtu5es66l0krSdwk6nSNtJ0ukUKTtF2l3fvK97O7Xd+mTzMplK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\nB4RuNWR6UNeEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f40ac11bf90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "_ = sns.kdeplot(df[df['class']==0]['vol_donations'],cumulative=True,label='0')\n",
    "_ = sns.kdeplot(df[df['class']==1]['vol_donations'],cumulative=True,label='1')\n",
    "_ = plt.xlabel('volume of blood donated')\n",
    "_ = plt.ylabel('CDF')\n",
    "_ = plt.title('CDF of monetary value of donors vs non-donors')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "The above CDF looks similar to the previous one. Frequency and monetary values are highly correlated. Here again, donars donate more volumne of blood than non-donors\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f40856a6990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GpgZKS8oYtPswamuG5jqsjFGPRkQkh1aurmtOMgCNTQ28OPPeourZKNGIiOTQkvq5zUkm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\nERHJKiUaERHJKiUaERHJqv8P5t/SlchhWjsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f408132aa10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10));\n",
    "_ = sns.lmplot(x='num_donations',\n",
    "               y='vol_donations',\n",
    "               hue='class',\n",
    "               fit_reg=False,\n",
    "               data=df);\n",
    "_ = plt.title(\"Correlation between frequency and monetary\");\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MfAakjCAzaThRkXGh20gR2U0BhoiIiIiI9At+v82WHU0s31LHZ1vqWFvYgN9v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6Jf99YcMDj53rd7a3p3raS8zPUrJEmSJKk5auoRGJcABwM7ATXAE8Aw4JxPua+aQDabJZ2poaau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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f40817b1850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "_ = sns.kdeplot(df[df['class']==0]['months_since_first_donation'],cumulative=True,label='0')\n",
    "_ = sns.kdeplot(df[df['class']==1]['months_since_first_donation'],cumulative=True,label='1')\n",
    "_ = plt.xlabel('months since first donation')\n",
    "_ = plt.ylabel('CDF')\n",
    "_ = plt.title('CDF of months since first donation of donors vs non-donors')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "CDF of months since first donations is not so differentiable between the two classes.\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAIDmCTAAAACA5gkwAAAAgOYJMAAAAIDmCTAAAACA5gkwAAAAgOYJMAAAAIDm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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4084fbf210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_long = pd.melt(df,id_vars='class',var_name='feature')\n",
    "plt.figure(figsize=(15,10))\n",
    "_ = sns.boxplot(x='feature',y='value',hue='class',data=df_long[df_long.feature!='vol_donations'])\n",
    "plt.margins(0.02)\n",
    "_ = plt.title('Boxplot of recency,frequency and time')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "we see that there are few outliers in recency and frequency for each of the classes. Should we remove them? What's the story behind these outliers?\n",
    "<br>\n",
    "<br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Prepare Data \n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "Let's split our dataset into training and testing. We'll drop 'monetary' feature from our training data since it has high correlation to 'frequency'. It is a good practise to standardise our data.\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train, x_test, y_train,y_test = train_test_split(df.drop(['class','vol_donations'],axis=1), df['class'], test_size = 0.1, random_state=2017)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "x_train_scaled = scaler.fit_transform(x_train)\n",
    "x_test_scaled = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-8.7982012847956928e-19, 1.0)"
      ]
     },
     "execution_count": 428,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_scaled.mean(),x_train_scaled.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.058943449584060933, 1.155008299202017)"
      ]
     },
     "execution_count": 429,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test_scaled.mean(),x_test_scaled.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Resampling and Visualizations \n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instanciate a PCA object for the sake of easy visualisation\n",
    "pca = PCA(n_components=2,random_state=RANDOM_STATE)\n",
    "# Fit and transform x to visualise inside a 2D feature space\n",
    "X_vis = pca.fit_transform(x_train_scaled)\n",
    "\n",
    "pca_original_df = pd.DataFrame(X_vis,y_train).reset_index()\n",
    "pca_original_df.columns = ['class','x','y']\n",
    "\n",
    "# Apply SMOTE + Tomek links\n",
    "sm = SMOTETomek()\n",
    "X_resampled, y_resampled = sm.fit_sample(x_train_scaled, y_train)\n",
    "X_res_vis = pca.transform(X_resampled)\n",
    "\n",
    "pca_resampled_df = pd.DataFrame(X_res_vis,y_resampled).reset_index()\n",
    "pca_resampled_df.columns = ['class','x','y']\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "**Visualizing original data in 2D**\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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N0j+RtCzpG5KKkp6Nx+O/0bPWAQAAAGjbAw88oF27dul3fud3tLKyokKhoJdeeqnqMYVC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\noef8//8mktmIBJQAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f408c5b3fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = sns.lmplot(x='x',\n",
    "               y='y',\n",
    "               hue='class',\n",
    "               x_jitter=True,\n",
    "               y_jitter=True,\n",
    "               fit_reg=False,\n",
    "               size=7,\n",
    "               aspect = 1.5,\n",
    "               data=pca_original_df,\n",
    "               scatter_kws={'alpha':0.5})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "**Visualizing resampled data in 2D**\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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v3/nBW9dVKM7LDozq+OGdGy6EuxV4uhVwah+nUHR1+WZWdsDUcDTQd3dvdLvvx++7UQAA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\nAACwnDABAACW+wNcrcTfm5EA7QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f408c44fbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = sns.lmplot(x='x',\n",
    "               y='y',\n",
    "               hue='class',\n",
    "               x_jitter=True,\n",
    "               y_jitter=True,\n",
    "               fit_reg=False,\n",
    "               size=7,\n",
    "               aspect = 1.5,\n",
    "               data=pca_resampled_df,\n",
    "               scatter_kws={'alpha':0.5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "\n",
    "I have tried building models on the resampled dataset, but the performance did not improve. Let's try doing some feature engineering on the dataset.\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>months_since_last_donation</th>\n",
       "      <th>num_donations</th>\n",
       "      <th>vol_donations</th>\n",
       "      <th>months_since_first_donation</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>12500</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>3250</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>4000</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>5000</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>6000</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   months_since_last_donation  num_donations  vol_donations  \\\n",
       "0                           2             50          12500   \n",
       "1                           0             13           3250   \n",
       "2                           1             16           4000   \n",
       "3                           2             20           5000   \n",
       "4                           1             24           6000   \n",
       "\n",
       "   months_since_first_donation  class  \n",
       "0                           98      1  \n",
       "1                           28      1  \n",
       "2                           35      1  \n",
       "3                           45      1  \n",
       "4                           77      0  "
      ]
     },
     "execution_count": 433,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "**submission**\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def create_submission(clf):\n",
    "    test = pd.read_csv(\"test.csv\")\n",
    "    test.columns = ['id','months_since_last_donation','num_donations','vol_donations','months_since_first_donation']\n",
    "    submit_id, submit_test = test['id'] , test.drop(['id','vol_donations'],axis=1)\n",
    "    submit_test_scaled = scaler.transform(submit_test) #scale the data\n",
    "    predictions = clf.predict_proba(submit_test_scaled)\n",
    "    predictions = predictions[:,1] #only predictions for class-1 needs to be submitted\n",
    "    pred_report = pd.DataFrame(predictions.tolist(),index=submit_id,columns=[\"Made Donation in March 2007\"])    \n",
    "    return pred_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Utility Functions\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Accent):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    plt.figure(figsize=(10,7))\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j],\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    plt.show()\n",
    "    return\n",
    "\n",
    "\n",
    "def prediction_report(true_label,predicted_label,classes=[0,1]):\n",
    "    \n",
    "    report = classification_report(true_label, predicted_label)\n",
    "    print \"classification report:\\n\",report\n",
    "    cnf_matrix = confusion_matrix(true_label, predicted_label)\n",
    "    np.set_printoptions(precision=2)\n",
    "    # Plot non-normalized confusion matrix\n",
    "    plt.figure(figsize=(15,10))\n",
    "    plot_confusion_matrix(cnf_matrix, classes=classes,title='Confusion matrix, without normalization')\n",
    "    return\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "---\n",
    "## Models\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LibSVM]"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC;\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier;\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "np.random.seed(2017)\n",
    "\n",
    "early_stopping_monitor = EarlyStopping(monitor='val_loss',patience=5)\n",
    "\n",
    "def create_mlp():\n",
    "    model = Sequential()\n",
    "    model.add(Dropout(0.25,input_shape=(x_train_scaled.shape[1],)))\n",
    "    model.add(Dense(100,activation='relu',input_shape=(x_train_scaled.shape[1],)))\n",
    "    model.add(Dropout(0.25))\n",
    "    model.add(Dense(100,activation='relu'))\n",
    "    model.add(Dropout(0.25))\n",
    "    model.add(Dense(100,activation='relu'))\n",
    "    model.add(Dense(2,activation='sigmoid'))\n",
    "    model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "\n",
    "svc = SVC(verbose=1,C=10,gamma=0.1,kernel='rbf',random_state=2017,probability=True);\n",
    "svc.fit(x_train_scaled,y_train);\n",
    "\n",
    "logit_model = LogisticRegression(random_state=2017)\n",
    "logit_model.fit(x_train_scaled,y_train)\n",
    "\n",
    "rf = RandomForestClassifier(n_estimators=100,max_depth=6,class_weight='balanced',n_jobs=4,random_state=2017)\n",
    "rf.fit(x_train_scaled,y_train)\n",
    "\n",
    "xgb = XGBClassifier(learning_rate=0.03,max_depth=5,n_estimators=250,reg_alpha=0.01)\n",
    "xgb.fit(x_train_scaled,y_train)\n",
    "\n",
    "mlp = KerasClassifier(build_fn=create_mlp, epochs=20, batch_size=10, validation_split=0.2,verbose=False)\n",
    "\n",
    "\n",
    "models = {'SVC': svc,\n",
    "          'Logistic Regression': logit_model,\n",
    "          'Random Forest': rf,\n",
    "          'XGBoost': xgb,\n",
    "          'mlp': mlp\n",
    "         }\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "Grid search for the best parameters of our models\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 48 candidates, totalling 144 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Done 128 tasks      | elapsed:  6.0min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LibSVM]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Done 144 out of 144 | elapsed: 10.1min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=True),\n",
       "       fit_params={}, iid=True, n_jobs=4,\n",
       "       param_grid={'kernel': ('linear', 'rbf'), 'C': [1, 10, 100, 1000], 'gamma': [0.1, 0.01, 0.001, 1, 10, 100]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring='neg_log_loss', verbose=True)"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10,100,1000], 'gamma': [1e-1,1e-2,1e-3,1,10,100]}\n",
    "svc_grid = SVC(probability=True,verbose=True)\n",
    "clf = GridSearchCV(svc_grid, parameters,scoring='neg_log_loss',verbose=True,n_jobs=4)\n",
    "clf.fit(x_train_scaled,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>param_C</th>\n",
       "      <th>param_gamma</th>\n",
       "      <th>param_kernel</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.489120</td>\n",
       "      <td>-0.470714</td>\n",
       "      <td>10</td>\n",
       "      <td>0.1</td>\n",
       "      <td>rbf</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.494660</td>\n",
       "      <td>-0.456200</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>rbf</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>-0.496060</td>\n",
       "      <td>-0.474736</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.01</td>\n",
       "      <td>rbf</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-0.497201</td>\n",
       "      <td>-0.459765</td>\n",
       "      <td>100</td>\n",
       "      <td>0.1</td>\n",
       "      <td>rbf</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.505502</td>\n",
       "      <td>-0.490678</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>rbf</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_test_score  mean_train_score param_C param_gamma param_kernel  \\\n",
       "13        -0.489120         -0.470714      10         0.1          rbf   \n",
       "7         -0.494660         -0.456200       1           1          rbf   \n",
       "39        -0.496060         -0.474736    1000        0.01          rbf   \n",
       "25        -0.497201         -0.459765     100         0.1          rbf   \n",
       "1         -0.505502         -0.490678       1         0.1          rbf   \n",
       "\n",
       "    rank_test_score  \n",
       "13                1  \n",
       "7                 2  \n",
       "39                3  \n",
       "25                4  \n",
       "1                 5  "
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(clf.cv_results_)[['mean_test_score',\n",
    "                               'mean_train_score',\n",
    "                               'param_C',\n",
    "                               'param_gamma',\n",
    "                               'param_kernel',\n",
    "                               'rank_test_score']].sort_values('rank_test_score')[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "**Cross-validation**\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation score\n",
      "=========================\n",
      "mlp   0.478453494027 +/- 0.0807720022143\n",
      "XGBoost   0.535469900327 +/- 0.100141966183\n",
      "SVC   0.491209526548 +/- 0.0481133206892\n",
      "Logistic Regression   0.47551648798 +/- 0.0601495385611\n",
      "Random Forest   0.556231217696 +/- 0.0740108456588\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(2017)\n",
    "print \"Cross-validation score\"\n",
    "print  \"=========================\"\n",
    "for model_name, model in models.items():\n",
    "    if model_name=='mlp':\n",
    "        cv_score = cross_val_score(estimator=model,X=x_train_scaled,y=to_categorical(y_train),\n",
    "                                   scoring='neg_log_loss',\n",
    "                                   cv=10,\n",
    "                                   n_jobs=4,\n",
    "                                   verbose=False,\n",
    "                                   fit_params={'callbacks':[early_stopping_monitor]})\n",
    "    else:\n",
    "        cv_score = cross_val_score(estimator=model,\n",
    "                                   X=x_train_scaled,\n",
    "                                   y=y_train,\n",
    "                                   cv=10,\n",
    "                                   n_jobs=4,\n",
    "                                   scoring='neg_log_loss',\n",
    "                                   verbose=False)\n",
    "    print model_name,\" \",-cv_score.mean(),\"+/-\",cv_score.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "**Test the models on test set**\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss\n",
      "==============\n",
      "mlp :  0.519841400829\n",
      "XGBoost :  0.592722008402\n",
      "SVC :  0.516236741052\n",
      "Logistic Regression :  0.52176733679\n",
      "Random Forest :  0.612271296484\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(2017)\n",
    "print \"Log loss\"\n",
    "print \"==============\"\n",
    "for model_name,model in models.items():\n",
    "    if model_name == 'mlp':\n",
    "        mlp.fit(x_train_scaled,to_categorical(y_train)) ##need to fit the model before predicting\n",
    "    y_pred = model.predict_proba(x_test_scaled)\n",
    "    print model_name,\": \",log_loss(y_test,y_pred)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "## Feature Engineering\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>months_since_last_donation</th>\n",
       "      <th>num_donations</th>\n",
       "      <th>vol_donations</th>\n",
       "      <th>months_since_first_donation</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>12500</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>3250</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>4000</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>5000</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>6000</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   months_since_last_donation  num_donations  vol_donations  \\\n",
       "0                           2             50          12500   \n",
       "1                           0             13           3250   \n",
       "2                           1             16           4000   \n",
       "3                           2             20           5000   \n",
       "4                           1             24           6000   \n",
       "\n",
       "   months_since_first_donation  class  \n",
       "0                           98      1  \n",
       "1                           28      1  \n",
       "2                           35      1  \n",
       "3                           45      1  \n",
       "4                           77      0  "
      ]
     },
     "execution_count": 435,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['num_donations_per_month'] = df['num_donations']*1.0/df['months_since_first_donation']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['months_between_first_n_last_donation'] = df['months_since_first_donation'] - df['months_since_last_donation']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>months_since_last_donation</th>\n",
       "      <th>num_donations</th>\n",
       "      <th>vol_donations</th>\n",
       "      <th>months_since_first_donation</th>\n",
       "      <th>class</th>\n",
       "      <th>num_donations_per_month</th>\n",
       "      <th>months_between_first_n_last_donation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>12500</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>0.510204</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>3250</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>0.464286</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>4000</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0.457143</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>5000</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>6000</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>0.311688</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   months_since_last_donation  num_donations  vol_donations  \\\n",
       "0                           2             50          12500   \n",
       "1                           0             13           3250   \n",
       "2                           1             16           4000   \n",
       "3                           2             20           5000   \n",
       "4                           1             24           6000   \n",
       "\n",
       "   months_since_first_donation  class  num_donations_per_month  \\\n",
       "0                           98      1                 0.510204   \n",
       "1                           28      1                 0.464286   \n",
       "2                           35      1                 0.457143   \n",
       "3                           45      1                 0.444444   \n",
       "4                           77      0                 0.311688   \n",
       "\n",
       "   months_between_first_n_last_donation  \n",
       "0                                    96  \n",
       "1                                    28  \n",
       "2                                    34  \n",
       "3                                    43  \n",
       "4                                    76  "
      ]
     },
     "execution_count": 438,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.  ,  0.94],\n",
       "       [ 0.94,  1.  ]])"
      ]
     },
     "execution_count": 448,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(df.months_since_first_donation,df.months_between_first_n_last_donation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {},
   "outputs": [
    {
     "data": {
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jEuCvwHZrse3DMcaPUtuOIJlcXxVjLI8xvggsALZcwz72B4pjjHfEGJfGGF8C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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f408134cdd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "corr = df.drop('class',axis=1).corr()\n",
    "plt.figure(figsize=(15,10))\n",
    "sns.heatmap(corr, \n",
    "            xticklabels=corr.columns.values,\n",
    "            yticklabels=corr.columns.values,\n",
    "            annot=True\n",
    "           );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}